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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to Do Structured JSON Line Logging in Python
Create a simple JSON-lines logger that writes one JSON object per line to stdout with timestamp, level, message, and custom context fields.
import json
import sys
from datetime import datetime
class JsonLineLogger:
def __init__(self, stream=sys.stdout):
self.stream = stream
def log(self, level, message, **context):
record = {
"timestamp": datetime.utcnow().isoformat() + "Z",
"level": level,
"me…
How to Do Structured JSON Logging in Python
Create a custom logging formatter that outputs each log entry as a single JSON line with timestamp, level, logger name, and message.
import json
import logging
from datetime import datetime
class JsonFormatter(logging.Formatter):
def format(self, record):
log_entry = {
"timestamp": datetime.utcnow().isoformat() + "Z",
"level": record.levelname,
"logger": record.name,
"message": record.ge…
How to Implement a Streaming Watermark in Python
Mock structured streaming watermarks in Python to track late event times and compute a watermark for windowed processing.
from datetime import datetime, timedelta
import time
class StreamingWatermark:
"""Mock watermark tracker for structured streaming."""
def __init__(self, watermark_delay_seconds):
self.watermark_delay = timedelta(seconds=watermark_delay_seconds)
self.max_event_time = None
def observe_even…
How to use foreachBatch with a mock sink in PySpark
Demonstrates using Spark Structured Streaming's foreachBatch sink to capture and verify streaming batches by writing them into a custom mock sink object.
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, lit
class MockSink:
def __init__(self):
self.batches = []
def write_batch(self, batch_df, batch_id):
# Collect batch data as list of dicts for verification
records = batch_df.collect()
self.batches…
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